Speaker
Description
The rapid advancement of large-scale language models (LLMs) is
bringing about a transformative era in language processing technology.
Traditionally, developing models that are capable of effectively
handling language expressions with the same level of freedom and
complexity as human intelligence has been extremely difficult.
Specifically, in the field of semantic parsing, there has been a
significant gap in achieving the performance required for real-world
applications. However, LLMs have greatly improved this situation and
are expanding the possibilities for social applications.
In the first part of this presentation, I will use the example of
ellipsis resolution, a benchmark task that requires a deep
understanding of semantic structures in texts and general knowledge,
to provide an overview of the challenges faced in the field of
semantic analysis and how neural language processing and LLMs have
partly addressed these challenges.
In the second part, I will present our efforts in language education
support technologies made possible by these technological
advancements, such as automated grading of short text answers and
writing assistance. I will discuss the considerations that need to be
taken into account when applying these technologies in practical
educational settings, including robustness, reliability, and
explainability.
